Nonparametric sequential prediction of time series
K Bleakley, G Biau, Laszlo Gyorfi and Gyorgy Ottucsak
Journal of Nonparametric Statistics
ISSN Print: 1048-5252 Online: 1029-0311
Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error.